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Project Overview: This application was built in four phases, each of which are described in greater detail below:

• Phase 1: Build a Convolutional Neural Network (CNN) to detect hand-drawn digits, & prepare it for general use,
• Phase 2: Train the model, & save it to make predictions,
• Phase 3: Develop remainder of full-stack application to integrate user input & prediction algorithm,
• Phase 4: Deploy Completed Application on a Platform as a Service (PaaS).

Dataset Overview:

• 'MNIST Database of handwritten digits' is maintained by Yann Lecun,
• Dataset includes 70,000 example images, which were used for this project.

Phase 1 - Backend - Building the CNN:

• Written in Python, utilizing keras machine-learning API with tensorflow backend (tf.keras), MatPlotLib, Pandas, NumPy,
• Developed in Jupyter Notebook File 'Development-CNN_Backend.ipynb',
• Model was manually built using keras 'Sequential' method,
• MNIST dataset was split into 60,000 training images & 10,000 validation images,
• Training process tested on single epoch, then prepared for next step (training locally was prohibitive due to processing power).

Phase 2 - Backend - Training Convolutional Neural Network: Utilising Cloud Processing Power in FloydHub:

• Jupyter Notebook File 'Development-CNN_Backend.ipynb' was converted into Python file 'training_app',
• Training data & python file uploaded to FloydHub (Machine Learning Platform), where the model was trained with the same data on 20 epochs,
• The model was saved to the cloud, then downloaded locally for the next step,
• (Model performance can be seen at the bottom of this section)
• Layers of Model:

Set	Loss	Accuracy
Training
Validation

Phase 3 - Full-Stack - Developing Interactive Application:

• Built with Node.js, node package manager (npm), bootstrap, & the gulp (JS toolkit) streaming build system,
• Developed in Jupyter Notebook File 'Development-Flask_Frontend.ipynb' & Converted into final python file 'app.py',
• Constructed as RESTful API, capturing user input, processing it, then displaying the prediction,
• Utilized Flask Web Micro-Framework, Werkzeug WSGI (Web Server Gateway Interface), Green Unicorn (Gunicorn) WSGI HTTP Server, Jinja2 templating engine, numPy, regular expressions, Pillow, Ajax, JavaScript, Event Listeners.

Phase 4 - Deploy to Heroku (PaaS):

• Prepare resources, Procfile, debug, finalize styling,
• perform final npm build,
• Deploy as standalone website.

Technologies: Machine Learning, tensorflow, keras, matplotlib, pandas, numpy, jupyter notebook, PaaS, FloydHub, Node.js, npm, bootstrap, gulp, RESTful API, WSGI, Gunicorn, Ajax, JavaScript, HTML, CSS, Pillow, Regular Expressions, Jinja2, WerkZeug.